| Title | mi-DS: Multiple-instance learning algorithm |
| Publication Type | Journal Article |
| Year of Publication | 2013 |
| Authors | Nguyen D.T. , Nguyen C.D. , Hargraves R. , Kurgan L.A. , Cios KJ |
| Journal | IEEE Transactions on Cybernetics |
| Volume | 43 |
| ISSN | 2168-2267 |
| Abstract | Multiple-instance learning (MIL) is a supervised learning technique that addresses the problem of classifying bags of instances instead of single instances. In this paper, we introduce a rule-based MIL algorithms, called mi-DS, and compare it with 21 existing MIL algorithms on 26 commonly used data sets. The results show that mi-DS performs on par with or better than several well-known algorithms and generates models characterized by balanced values of precision and recall. Importantly, the introduced method provides a framework that can be used for converting other rule-based algorithms into MIL algorithms. |
